Noise predictive maximum likelihood (NPML) system architecture has been extensively used by the magnetic recording field for hard disk drive (HDD) storage systems. NPML embeds a noise predictor in the sequence detection process and utilizes the decisions from each hypothesized path as a means to reliably estimate the noise samples. As a result, the embedded noise predictor de-correlates the noise and reduces its power in a very effective way, thereby improving the noise margin and/or substantially increasing the linear density of the recording system. This basic concept has been extended to include other forms of noise, notably data-depended noise, as well as other nonlinear effects that are present in the magnetic recording process. For example, a hybrid NPML detection system comprising a combination of FIR/IIR embedded predictors with an adaptive table-look-up based branch-metric computation has been proposed.
In magnetic tape recording systems, the noise process at the detector input is strongly data-dependent. Surface roughness, a phenomenon that manifests itself during the recording as well as during the readback process, is known to be one of the main reasons for data-dependent noise in tape systems. However, other phenomena also exist that contribute to creating a data-dependent noise component in the readback signal. These include transition or media noise, which is closely related to the granularity of the magnetic medium, and also residual intersymbol interference after signal equalization, which creates an undesired signal component that can be regarded as data-dependent noise.